Continuous Learning of a Multilayered Network Topology in a Video Camera Network. (16th November 2009)

• Record Type: Journal Article
• Authors: Zou, Xiaotao; Bhanu, Bir; Roy-Chowdhury, Amit
• Other Names: Boulgouris Nikolaos V. Academic Editor.
• Is Part Of: EURASIP journal on image and video processing. Volume 2009(2009)
• Publication Date: 2009-11-16
• Subjects: Image processing -- Digital techniques -- Periodicals; Digital video -- Periodicals; Traitement d'images; Vidéo numérique; Digital video; Image processing -- Digital techniques; Periodicals; Electronic journal; Electronic journals; 621.367
• DOI: 10.1155/2009/460689
• Languages: English
• ISSNs: 1687-5176

Abstract

Abstract : A multilayered camera network architecture with nodes as entry/exit points, cameras, and clusters of cameras at different layers is proposed. Unlike existing methods that used discrete events or appearance information to infer the network topology at a single level, this paper integrates face recognition that provides robustness to appearance changes and better models the time-varying traffic patterns in the network. The statistical dependence between the nodes, indicating the connectivity and traffic patterns of the camera network, is represented by a weighted directed graph and transition times that may have multimodal distributions. The traffic patterns and the network topology may be changing in the dynamic environment. We propose a Monte Carlo Expectation-Maximization algorithm-based continuous learning mechanism to capture the latent dynamically changing characteristics of the network topology. In the experiments, a nine-camera network with twenty-five nodes (at the lowest level) is analyzed both in simulation and in real-life experiments and compared with previous approaches.